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A novel approach is devised for the quantification of systematic uncertainty due to peak locking in particle image velocimetry (PIV), which also leads to correction of the peak-locking errors. The approach, applicable to statistical flow properties such as time-averaged velocity and Reynolds stresses, relies on image recordings with multiple time separations Δt and a least-squares regression of the measured quantities. In presence of peak locking, the measured particle image displacement is a non-linear function of Δt due to the presence of measurement errors which vary non-linearly with the sub-pixel particle image displacement. Additionally, the measured displacement fluctuations are a combination of the actual flow fluctuations and the measurement error. When the image recordings are acquired with multiple Δt’s, a least-squares regression among the statistical results yields a correction where systematic errors due to peak locking are significantly diminished. The methodology is assessed for planar PIV measurements of the flow over a NACA0012 airfoil at 10 degrees angle of attack. Reference measurements with much larger Δt than the Δt’s of the actual measurements, such that relative peak-locking errors are negligible for the former, are used to assess the validity of the proposed approach.

Multi-Δt approach for peak-locking error correction and uncertainty quantification in PIV